TestMAPlot

### TEST: glimmav1 dataset (MArrayLM) ###

library(Glimma)
library(limma)
library(GlimmaV2)

data(lymphomaRNAseq)
rnaseq <- lymphomaRNAseq

# add lane
groups <- data.frame(genotype=rnaseq$samples$group,
                     lane= as.character(c(rep(4,5),3,3)),
                     miscCont=c(rep(4000,5),300,250),
                     miscDisc=c("blue","red",rep("green",5)))

# add libsize
groups <- cbind(groups, libsize=rnaseq$samples$lib.size)

# fit
design <- model.matrix(~0+rnaseq$samples$group)
contrasts <- cbind(Smchd1null.vs.WT=c(-1,1))

# convert raw counts to logCPM values by automatically extracting libsizes and normalisation factors from x
vm <- voomWithQualityWeights(rnaseq, design=design)
fit <- lmFit(vm, design=design)
fit <- contrasts.fit(fit, contrasts) 
fit <- eBayes(fit)
dtFit <- decideTests(fit)

# general plot
glimmaMA(fit, dge=rnaseq, width=NULL, height=NULL)